System and method for conducting a game including a computer-controlled player

Abstract

A system and method for conducting a game between at least one live player and at least one computer-controlled player includes executing a training program between at least two agents to generate probability weights correlating actions or meta-actions representing a set or sequenced set of actions with a probability that the action or meta-action will produce a game outcome meeting a specified criterion or specified criteria. A game is conducted in which at least one live player plays against at least one computer-controlled player in which the computer-controlled player selects actions at one or more of the decision nodes in the game based, at least in part, on the probability weights.

What is claimed is: 
     
       1. A method for use by a computer system for conducting a game between at least one computer-controlled player and at least one live player comprising:
 receiving, by the computer system, a plurality of predictions from a neural network, wherein the predictions represent a likelihood that each of a plurality of available actions will satisfy a predetermined criterion; 
 selecting, by the computer system, one of the available actions for the computer-controlled player, wherein each of the available actions has a percentage chance of being selected that is equal to the likelihood of that available action satisfying the predetermined criterion; and 
 evaluating, by the computer system, an outcome of a decision node in the game based on one of the available actions selected by a live player and the available action randomly selected for the computer-controlled player. 
 
     
     
       2. The method of  claim 1 , wherein the predetermined criterion is a minimization of maximum loss criterion. 
     
     
       3. The method of  claim 1 , wherein the likelihood of each action being selected is based on a probability weight generated by a neural network training program. 
     
     
       4. The method of  claim 1 , further comprising:
 identifying, by the computer system, that the decision node in the game has been reached; and 
 providing, by the computer system, a plurality of inputs to the neural network, wherein the inputs include a face value and suit of each of a plurality of community cards, a face value and suit of at least one computer-controlled card, and the plurality of available actions. 
 
     
     
       5. The method of  claim 4 , further comprising:
 identifying, by the computer system, a game state of a plurality of game states at the decision node; and 
 using, by the computer system, a predetermined probability distribution of probability weights corresponding to the identified game state for the available actions. 
 
     
     
       6. The method of  claim 5 , wherein wagers are made at decision nodes during the game, and wherein the wagers are contributed to a pot. 
     
     
       7. The method of  claim 6 , further comprising determining the outcome and distributing at least a portion of the pot to the live player. 
     
     
       8. The method of  claim 4 , wherein at least one of the available actions is a meta-action that represents a sequence of two or more actions. 
     
     
       9. The method of  claim 8 , wherein only a first portion of the sequence of two or more actions is executed at the decision node. 
     
     
       10. The method of  claim 9 , wherein a second portion of the sequence is executed at another decision node. 
     
     
       11. The method of  claim 10 , wherein execution of the second portion of the sequence at another decision node is conditional. 
     
     
       12. A system for conducting a game between at least one computer-controlled player and at least one live player comprising:
 a data processor; 
 a data storage; and 
 a plurality of instructions stored in the data storage and executable by the data processor, the instructions including: 
 instructions for receiving, by the system, from a neural network, a likelihood that each of a plurality of available actions will satisfy a predetermined criterion based on a game state; and 
 instructions for selecting one of the available actions for the computer-controlled player, wherein each of the available actions has a likelihood of being selected that is equal to the likelihood of that available action satisfying the predetermined criterion. 
 
     
     
       13. The system of  claim 12 , wherein the predetermined criterion is a minimization of maximum loss criterion. 
     
     
       14. The system of  claim 13 , further comprising instructions for receiving wagers at decision nodes during the game, and instructions for adding the wagers are to a pot. 
     
     
       15. The system of  claim 14 , further comprising instructions for determining a game outcome and instructions for distributing at least a portion of the pot to a live player. 
     
     
       16. The system of  claim 12 , further comprising:
 instructions for identifying that a decision node in the game has been reached; and 
 instructions for providing the game state at the decision node and the plurality of available actions to the neural network. 
 
     
     
       17. The system of  claim 16 , further comprising:
 instructions for identifying a game state of a plurality of game states at the decision node; and 
 instructions for using a predetermined probability distribution of probability weights corresponding to the identified game state for the available actions. 
 
     
     
       18. The system of  claim 16 , wherein at least one of the available actions is a meta-action that represents a sequence of two or more actions. 
     
     
       19. The system of  claim 18 , wherein only a first portion of the sequence is executed at the decision node. 
     
     
       20. The system of  claim 19 , wherein a second portion of the sequence is executed at another decision node.